RandomForestSolver: Create a Solver class object using the Random Forest solver

In PriceLab/trena-until-01mar2018: Fit transcriptional regulatory networks using gene expression, priors, machine learning

Description

Create a Solver class object using the Random Forest solver

Usage

RandomForestSolver(mtx.assay = matrix(), targetGene, candidateRegulators,
  regulatorWeights = rep(1, length(candidateRegulators)), quiet = TRUE)

Arguments

mtx.assay	An assay matrix of gene expression data
targetGene	A designated target gene that should be part of the mtx.assay data
candidateRegulators	The designated set of transcription factors that could be associated with the target gene
regulatorWeights	A set of weights on the transcription factors (default = rep(1, length(candidateRegulators)))
quiet	A logical denoting whether or not the solver should print output

Value

A Solver class object with Random Forest as the solver

See Also

solve.RandomForest, getAssayData

Other Solver class objects: BayesSpikeSolver, EnsembleSolver, HumanDHSFilter-class, LassoPVSolver, LassoSolver, PearsonSolver, RidgeSolver, Solver-class, SpearmanSolver, SqrtLassoSolver

Examples

load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
targetGene <- "MEF2C"
candidateRegulators <- setdiff(rownames(mtx.sub), targetGene)
rf.solver <- RandomForestSolver(mtx.sub, targetGene, candidateRegulators)

PriceLab/trena-until-01mar2018 documentation built on May 25, 2019, 1:22 p.m.